For the objective to predict the actual performance of the system through a simulated model is known as simulation. In this, it specifically analyzes the real behavior of the system in different scenarios and conditions. This simulation support data analysis and mathematical modeling. In the case of AI, simulation provides you with add-on information for processing machine learning techniques to gain enriched knowledge on data. 

From this article, you can acquire interesting research facts on both Computer Simulation and Artificial Intelligence along with development info!!!

Before getting into the section in deep, first of all, realize the key difference between the standard AI model and simulation model. On the one hand, artificial intelligence models are referred to as “data-centric” tasks. On the other hand, simulation models are referred to as “process-centric” tasks. Let’s see what exactly these models differ with.

• Artificial Intelligence Model
  • To design an AI model, initially, the modeler wants to clear with involved data from data scientists. This helps the modeler to gain situational awareness for making decisions accurately
• Simulation Model
  • To design a simulation model, initially, the modeler wants to clear with involved processes from decision-makers and operators. This helps the modeler to collect related data for system observation

Now, we can see about the mathematical modeling which is more important for simulating models. The simulation models are intended to create a realistic environment with a virtual reality experience. To work with different forms of data and processes like sound, light, signal, motion which act as a tool to gain human perception. Then the results will be mathematically functioned in a virtual way to support computer simulation and artificial intelligence. These processes are achieved only by mathematical models.

Our developers have years of experience in performing numerical analysis for building mathematical modeling. In specific, we are strong from numerical linear algebra to convex programming. Further, we also extend our support in the deterministic and stochastic gradient descent concepts. Overall, we support you in all practical execution of your mathematical theorems at a level of intricacy. In this math context, here we have given some artificial intelligence applications for your reference.

4 Different Application Areas of AI

• Computer Vision
• Medical imaging
• Environmental Learning
• Time Series Analysis

Mathematical modeling and coding are currently creating a major beneficial impact on Computer Simulation and Artificial Intelligence. In specific, it strengthens the performance of AI applications in all aspects despite the complexity. Also, it provides so many novel and effective solutions for so many problems using standard packages. For instance, OpenSceneGraph, GeneSim,. OPAL etc. helps to enhance the performance of simulation in terms of energy, gaming, and communication. Further, here we have also given you some key terminologies that you need to know while researching artificial intelligence.

Definition of AI Simulation Terms

• System Specification
  • It represents the formulation of specification for systems
  • In specific, it addresses from AI behavior issues
• System-Level Specification
  • It represents the levels of system between behavior and structure
  • In specific, it is used in the case of dynamic input and output operations over AI systems
• Automated AI Model
  • It represent the systematic development of AI models
  • In specific, it uses system specification for developing artifacts

Next, we can see the two main phases of AI model simulation. Each phase has a particular set of functionalities to achieve certain objectives. Our developers are well-practiced in handling AI simulation in different infrastructures, conditions, configurations, varying topology, and scenarios. So, we are keen to direct you on the right track of computer simulation and artificial intelligence. Our overall aim is to simulate a model with high performance and expected results at any level of complication. Further, we are also good to adjust the stimulation parameters for improving the system efficiency. Let’s see about the main phases of AI simulation.

How does computer simulation works for AI?

• Model Generation
  • Create the model with necessary components and settings
• Model Application
  • Perform the main simulation functions and generate experimental results

Relatively, phase 2 needs more concern than phase 1. The overall entities of the AI model are input parameters, simulation model, techniques, numerical functions, and simulation outcome. Generally, the fundamental simulation model defines the local interaction between the initial model and data space. As well, the execution of the model employs “top-down” approaches. Here, we have included the primary operations of the basic AI model for your reference.

• Data Generation
  • Take random number as input
  • Perform preprocessing / meshing
  • Solve the data related issues
  • Perform postprocessing
• System Designing
  • Select the model
  • Select quality parameters and least square fit() to formulate model equation
• System Analysis
  • Analyze model by sensitivity-study
  • Search target by characteristics
  • Optimize the analysis process for betterment
• System Validation
  • Collect parameter feedback for initializing numerical model
  • Validate the model techniques for accomplishing best result

When the simulation is complete, the experimental results will be generated. As well, these simulation results can be used for the next level of process or assessment or comparative study, etc. We assure you that our produced results will surely meet your expectation by achieving your research/project goal.  Here, we have given you some valuable usages of experimental results from different perspectives.

The simulation results of AI can be used to,

• Describe empirical methods as hypothesis set
• Enhance the data for training
• Validate the obtained hypothesis over technical constancy
• Analyze the training technique in generative adversarial systems

Although Computer Simulation and Artificial Intelligence field is forwarding towards future technologies, it has some technical impact on real implementation. In order to improve the performance of the system, one should take necessary measures against those technical issues. Here, we have given you 3 primary questions that you have to be answered while validating the AI framework.

Top 3 Research Issues in AI simulation

• Whether the attained precision enables optimization for accurate and useful results?
• Whether the learning tool suits for emulating complex network. If suits, which tool gives you accurate result on emulation?
• What number of training and testing samples is required for learning model to achieve robust assessment?

In point of fact, AI enables you to use and process a large volume of data in the industrial sector. So, the development of different AI frameworks and tools are increasing gradually to support developers and data scientist for implementing computer simulation and artificial intelligence projects. Moreover, this software development is also greatly influenced by advancements in machine learning and artificial intelligence fields. Our developers are passionate to work on advanced and emerging tools to give you modern research outcomes. In general, AI tools and technologies are classified under the following types.

Types of AI Software

• Chatbots
  • It supports to establish human conversation through text messages
• AI Frameworks
  • It supports to develop artificial intelligence applications from scratch
  • It is sophisticated with massive pre-defined algorithms, techniques and functions
• ML Tool
  • It supports different machine learning algorithms for making systems to learn independently
• DL Platform
  • It supports learning of complex patterns through neural networks. For instance – image scene recognition, object detection, etc.

In addition, we have given you some extensively used programming languages for developing artificial intelligence projects. Similar to technologies, our developers are also great in code simplification even for complex problems. So, we are able to develop any sorts of complicated issues without affecting the system performance. Since, we are good at recognizing suitable packages, modules, and libraries for your project. And also, we design our own algorithm and hybrid techniques in the case of requirements.

Programming Languages for AI

• Java
• R Programming
• Haskell
• Python AI Projects
• Javascript
• C++
• Julla

AI Libraries in Java

For illustration purposes, here we have taken “Java” as an example from the above list for implementation of computer simulation and artificial intelligence projects. Basically, Java is sophisticated with number libraries and packages. Here, we have listed a few main libraries/frameworks based on the significant AI techniques/models. In this, we also included the key operations of each library/framework. Further, we also recommend you best-fitting libraries/framework for your projects based on requirements. We assure you that our proposed libraries will simplify your development task with the best results.

• Machine Learning
  • RapidMiner
    • It is a framework to execute ML algorithms using Java API and GUI
  • Encog
    • It is a platform with more advanced ML techniques
  • Java-ML
    • It is a tool with greater number of machine learning techniques
  • Weka
    • It is a software to support all machine learning algorithms
• Automatic Programming
  • Acceleo
    • It is a generator to produce code for EMF models (Eclipse)
  • Spring Roo
    • It is lightweight and developer-friendly tool to build automatic programming
• Genetic Algorithms
  • ECJ 23
    • It provides platform to handle genetic algorithms to support researchers
  • Eva
    • It gives framework for supporting evolutionary algorithm and basic Object Oriented Programming
  • Watchmaker
    • It provides platform for developing and executing genetic algorithms.
  • Java Genetic Algorithms Package (JGAP)
    • It provides package to enable genetic programming
  • Jenetics
    • It is a framework to flexibly work with  advanced genetic algorithm.
• Natural Language Processing
  • Stanford CoreNLP
    • It provides a platform to execute natural language processing tasks
  • Apache OpenNLP
    • It provides all essential tools to work with natural language text for specific processes
• Neural Networks
  • Deeplearning4j
    • It is a built-in library which specifically introduced for deep learning to run on JVM
    • It also bestows with more APIs which suits for creation of neural network
  • Neuroph
    • It is an open-software to build and test different neural network algorithms
• Expert Systems
  • Tweety
    • It comprises set of frameworks for developing models to represent knowledge and AI logics
  • PowerLoom
    • It provides framework to construct logical-reasoning models and learning-based applications
  • Eye
    • It provides platform for executing semi-backward reasoning using reasoning engine
  • d3web
    • It provides infrastructure to design reasoning engine and techniques to crack the AI issues
  • Apache Jena
    • It provides the platform to design and develop web applications and networked systems

In addition, we have also given you a few primary implementation tools that are widely employed for computer simulation and artificial intelligence. By analyzing the proposed functionalities of your project, we suggest you optimal implementation tool for your project. Most importantly, we support you not only with these tools but also with other advanced development platforms and tools.

What are the simulation tools for AI?

• Scilab
• OpenCV
• Python
  • SciPy
  • Numpy
• NS3
  • NS3Gym
• MATLAB
  • Fuzzy Logic Toolbox
  • Deep Learning Toolbox

For instance, discussed the NS3Gym for reference,

For illustration purposes, here we have taken “Ns3-gym” as an example from the above-specified list. Actually, it is a toolkit obtained from the NS3 simulation tool which extended from Open AI Gym frameworks. In specific, this toolkit comprises two main entities as environment proxy (python) and environment gateway (C++).

The main objective of this toolkit is to make the networking processes easier as possible. In specific, it is used to train agents of reinforcement learning and to develop networking infrastructure. Further, it also supports other common processes through enriched APIs. Moreover, Ns3-gym has some unique aspects compare to others. Some of them are managing the life-cycle of the network simulation process, pause the simulation while agents communicate, and efficient data communication. For more understanding, here we have given you the steps involved in the development of RL-based agents training using NS-3.

Simulation Steps of NS3-Gym

• Form the network infrastructure with scenario configuration. For instance – mobility, traffic, etc.
• Make OpenGymGateway object and apply callbacks functions to acquire environ conditions for agaent interaction
• Use Gym::make(’ns3-gym’) function to construct environment proxy
• Use python-based libraries and functions like standard Gym::step(action) function to make RL-based agent to interact with environ
• Perform training operations over the agent and then test the simulation using the input features / data points

Further, if you are looking for more information on the development of Computer Simulation and Artificial Intelligence systems then reach us. Our experts from resource team give you the best guidance in handpicking unique project topic from top research areas and developing into an incredible research project. In truth, we are unique in solving your research problems through smart approaches. So, make use of the chance to glow up in your research profession from others. We assure you that, we will stand with you till you reach your targeted research destination.